dennisthiessen
1e82dfad7f
Production strategy change based on the July 2026 backtest: paper trades now default to a 30-trading-day hold with the initial stop (classic momentum hold-and-rerank), while target and trailing exits remain available in Admin. The exit policy API/UI now carries hold_days and close_reason can be 'time'. The activation confidence floor default is now 0/off because the gate ablation showed it added no per-trade edge while filtering out usable setups. Migration 015 clears stored activation_min_confidence and paper_exit_mode so the new defaults take effect; this intentionally resets Track Record comparability from this deploy. Verification: 451 backend tests pass, ruff check app/ clean, frontend npm run build clean. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
404 lines
15 KiB
Python
404 lines
15 KiB
Python
"""Paper-trading service: take, mark-to-market, and close simulated trades."""
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from __future__ import annotations
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from datetime import date, datetime, timezone
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from sqlalchemy import and_, func, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.exceptions import NotFoundError, ValidationError
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from app.models.ohlcv import OHLCVRecord
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from app.models.paper_trade import PaperTrade
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from app.models.ticker import Ticker
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from app.services import benchmark_service, settings_store
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from app.services.outcome_service import (
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OUTCOME_AMBIGUOUS,
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OUTCOME_STOP_HIT,
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OUTCOME_TARGET_HIT,
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Bar,
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evaluate_setup_against_bars,
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)
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# Exit policy for OPEN paper trades (auto-close). "time" holds a fixed number of
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# trading days with the initial stop and exits at that day's close — the exit the
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# July 2026 backtest validated (the classic momentum hold-and-re-rank); "trailing"
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# rides a trailing stop; "target" closes at the setup's stop/target. Stored in
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# SystemSetting so it's tunable + transparent in the UI.
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KEY_EXIT_MODE = "paper_exit_mode"
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KEY_TRAILING_PCT = "paper_trailing_pct"
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KEY_HOLD_DAYS = "paper_hold_days"
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DEFAULT_EXIT_MODE = "time"
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DEFAULT_TRAILING_PCT = 12.0
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DEFAULT_HOLD_DAYS = 30
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_VALID_EXIT_MODES = ("time", "trailing", "target")
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async def get_exit_policy(db: AsyncSession) -> dict:
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"""Active auto-exit policy:
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{'mode': 'time'|'trailing'|'target', 'trailing_pct': float, 'hold_days': int}."""
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mode = (await settings_store.get_value(db, KEY_EXIT_MODE, DEFAULT_EXIT_MODE)).strip().lower()
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if mode not in _VALID_EXIT_MODES:
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mode = DEFAULT_EXIT_MODE
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raw = await settings_store.get_value(db, KEY_TRAILING_PCT, str(DEFAULT_TRAILING_PCT))
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try:
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pct = float(raw)
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except (TypeError, ValueError):
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pct = DEFAULT_TRAILING_PCT
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pct = max(0.5, min(90.0, pct))
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raw_days = await settings_store.get_value(db, KEY_HOLD_DAYS, str(DEFAULT_HOLD_DAYS))
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try:
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hold_days = int(float(raw_days))
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except (TypeError, ValueError):
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hold_days = DEFAULT_HOLD_DAYS
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hold_days = max(2, min(250, hold_days))
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return {"mode": mode, "trailing_pct": pct, "hold_days": hold_days}
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async def set_exit_policy(
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db: AsyncSession,
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*,
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mode: str | None = None,
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trailing_pct: float | None = None,
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hold_days: int | None = None,
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) -> dict:
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"""Persist the auto-exit policy (admin). Validates inputs."""
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if mode is not None:
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mode = mode.strip().lower()
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if mode not in _VALID_EXIT_MODES:
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raise ValidationError("mode must be 'time', 'trailing' or 'target'")
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await settings_store.upsert_setting(db, KEY_EXIT_MODE, mode)
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if trailing_pct is not None:
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if not 0.5 <= float(trailing_pct) <= 90.0:
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raise ValidationError("trailing_pct must be between 0.5 and 90")
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await settings_store.upsert_setting(db, KEY_TRAILING_PCT, str(float(trailing_pct)))
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if hold_days is not None:
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if not 2 <= int(hold_days) <= 250:
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raise ValidationError("hold_days must be between 2 and 250")
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await settings_store.upsert_setting(db, KEY_HOLD_DAYS, str(int(hold_days)))
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await db.commit()
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return await get_exit_policy(db)
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async def _get_ticker(db: AsyncSession, symbol: str) -> Ticker:
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normalised = symbol.strip().upper()
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result = await db.execute(select(Ticker).where(Ticker.symbol == normalised))
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ticker = result.scalar_one_or_none()
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if ticker is None:
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raise NotFoundError(f"Ticker not found: {normalised}")
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return ticker
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async def _latest_closes(db: AsyncSession, ticker_ids: set[int]) -> dict[int, float]:
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"""Latest stored close per ticker."""
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if not ticker_ids:
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return {}
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latest = (
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select(OHLCVRecord.ticker_id, func.max(OHLCVRecord.date).label("md"))
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.where(OHLCVRecord.ticker_id.in_(ticker_ids))
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.group_by(OHLCVRecord.ticker_id)
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.subquery()
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)
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stmt = select(OHLCVRecord.ticker_id, OHLCVRecord.close).join(
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latest,
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and_(
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OHLCVRecord.ticker_id == latest.c.ticker_id,
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OHLCVRecord.date == latest.c.md,
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),
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)
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result = await db.execute(stmt)
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return {tid: float(close) for tid, close in result.all()}
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async def _max_high_after(db: AsyncSession, ticker_id: int, since: date) -> float | None:
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"""Highest high strictly after ``since`` — the running peak for a trailing stop."""
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result = await db.execute(
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select(func.max(OHLCVRecord.high)).where(
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OHLCVRecord.ticker_id == ticker_id, OHLCVRecord.date > since
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)
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)
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v = result.scalar()
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return float(v) if v is not None else None
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def _time_close(
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direction: str, init_stop: float, hold_days: int, rows: list[tuple]
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) -> tuple[float, date, str] | None:
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"""Walk post-entry ``rows`` of (date, open, high, low, close); close at the
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initial stop if hit (a gap through it fills at the open, matching the
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backtest's fill model), else at the ``hold_days``-th bar's close ('time').
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None while neither has happened."""
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long = direction == "long"
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for i, (d, open_, high, low, close) in enumerate(rows):
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if (low <= init_stop) if long else (high >= init_stop):
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fill = min(init_stop, open_) if long else max(init_stop, open_)
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return float(fill), d, "stop"
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if i + 1 >= hold_days:
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return float(close), d, "time"
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return None
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def _trailing_close(
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direction: str, entry: float, init_stop: float, trail_frac: float, bars: list[Bar]
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) -> tuple[float, date, str] | None:
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"""Walk post-entry bars; return (price, date, reason) when the trailing or initial
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stop is hit, else None. The stop only ratchets up: max(init_stop, peak*(1-trail))
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for a long. reason = 'trailing' once it's above the initial stop, else 'stop'."""
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long = direction == "long"
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peak = entry
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for b in bars:
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if long:
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level = max(init_stop, peak * (1 - trail_frac))
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if b.low <= level:
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return level, b.date, ("trailing" if level > init_stop else "stop")
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if b.high > peak:
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peak = b.high
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else:
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level = min(init_stop, peak * (1 + trail_frac))
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if b.high >= level:
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return level, b.date, ("trailing" if level < init_stop else "stop")
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if b.low < peak:
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peak = b.low
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return None
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async def create_trade(
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db: AsyncSession,
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user_id: int,
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*,
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symbol: str,
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direction: str,
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entry_price: float,
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shares: float,
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stop_loss: float,
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target: float,
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) -> PaperTrade:
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direction = direction.strip().lower()
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if direction not in ("long", "short"):
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raise ValidationError("direction must be 'long' or 'short'")
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if shares <= 0 or entry_price <= 0:
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raise ValidationError("shares and entry_price must be positive")
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ticker = await _get_ticker(db, symbol)
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trade = PaperTrade(
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user_id=user_id,
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ticker_id=ticker.id,
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direction=direction,
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entry_price=entry_price,
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shares=shares,
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stop_loss=stop_loss,
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target=target,
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status="open",
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opened_at=datetime.now(timezone.utc),
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)
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db.add(trade)
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await db.commit()
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await db.refresh(trade)
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return trade
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def _to_dict(
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trade: PaperTrade,
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symbol: str,
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current_price: float | None,
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benchmark_closes: dict[date, float] | None = None,
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trailing: tuple[float, float | None] | None = None,
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) -> dict:
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# For open trades, mark to market; for closed, the realized exit price.
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ref = current_price if trade.status == "open" else trade.close_price
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# Alpha = trade return − benchmark (SPY) return over the same holding period.
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benchmark_return = None
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alpha_pct = None
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alpha_usd = None
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if ref is not None and trade.entry_price and benchmark_closes:
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sign = 1.0 if trade.direction == "long" else -1.0
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trade_return = (ref - trade.entry_price) / trade.entry_price * 100.0 * sign
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as_of = (
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trade.closed_at.date()
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if trade.status == "closed" and trade.closed_at is not None
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else date.today()
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)
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benchmark_return = benchmark_service.benchmark_return_pct(
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benchmark_closes, trade.opened_at.date(), as_of
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)
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if benchmark_return is not None:
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alpha_pct = trade_return - benchmark_return
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alpha_usd = alpha_pct / 100.0 * trade.entry_price * trade.shares
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return {
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"id": trade.id,
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"symbol": symbol,
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"direction": trade.direction,
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"entry_price": trade.entry_price,
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"shares": trade.shares,
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"stop_loss": trade.stop_loss,
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"target": trade.target,
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"status": trade.status,
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"opened_at": trade.opened_at,
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"close_price": trade.close_price,
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"closed_at": trade.closed_at,
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"current_price": ref,
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"benchmark_return_pct": benchmark_return,
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"alpha_pct": alpha_pct,
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"alpha_usd": alpha_usd,
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"close_reason": trade.close_reason,
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"trailing_stop": trailing[0] if trailing else None,
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"trailing_distance_pct": trailing[1] if trailing else None,
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}
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async def list_trades(
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db: AsyncSession,
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user_id: int | None = None,
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status: str | None = None,
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) -> list[dict]:
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stmt = (
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select(PaperTrade, Ticker.symbol)
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.join(Ticker, PaperTrade.ticker_id == Ticker.id)
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)
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if user_id is not None: # None → all users (single-user app; used by the digest)
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stmt = stmt.where(PaperTrade.user_id == user_id)
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if status is not None:
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stmt = stmt.where(PaperTrade.status == status)
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stmt = stmt.order_by(PaperTrade.opened_at.desc())
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rows = (await db.execute(stmt)).all()
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open_ids = {t.ticker_id for t, _ in rows if t.status == "open"}
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prices = await _latest_closes(db, open_ids)
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# Benchmark closes for alpha — populated by the daily/benchmark job. Empty until
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# that runs once, in which case alpha is simply left unset (a read path never
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# makes a provider call).
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benchmark_closes = await benchmark_service.load_benchmark_closes(db)
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# Current trailing-stop level + distance for open trades (when trailing is active).
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policy = await get_exit_policy(db)
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trailing_info: dict[int, tuple[float, float | None]] = {}
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if policy["mode"] == "trailing":
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trail_frac = policy["trailing_pct"] / 100.0
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for t, _ in rows:
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if t.status != "open":
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continue
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max_high = await _max_high_after(db, t.ticker_id, t.opened_at.date())
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peak = max(t.entry_price, max_high) if max_high is not None else t.entry_price
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long = t.direction == "long"
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level = (
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max(t.stop_loss, peak * (1 - trail_frac))
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if long
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else min(t.stop_loss, peak * (1 + trail_frac))
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)
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cur = prices.get(t.ticker_id)
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dist = None
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if cur:
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dist = ((cur - level) / cur * 100.0) if long else ((level - cur) / cur * 100.0)
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trailing_info[t.id] = (level, dist)
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return [
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_to_dict(t, sym, prices.get(t.ticker_id), benchmark_closes, trailing_info.get(t.id))
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for t, sym in rows
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]
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async def close_trade(
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db: AsyncSession,
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user_id: int,
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trade_id: int,
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close_price: float | None = None,
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) -> PaperTrade:
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result = await db.execute(
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select(PaperTrade).where(
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PaperTrade.id == trade_id,
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PaperTrade.user_id == user_id,
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)
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)
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trade = result.scalar_one_or_none()
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if trade is None:
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raise NotFoundError(f"Paper trade not found: {trade_id}")
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if trade.status == "closed":
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raise ValidationError("Trade is already closed")
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if close_price is None:
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prices = await _latest_closes(db, {trade.ticker_id})
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close_price = prices.get(trade.ticker_id)
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if close_price is None:
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raise ValidationError("No current price available to close at; supply close_price")
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trade.status = "closed"
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trade.close_price = float(close_price)
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trade.close_reason = "manual"
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trade.closed_at = datetime.now(timezone.utc)
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await db.commit()
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await db.refresh(trade)
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return trade
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async def resolve_open_trades(db: AsyncSession) -> int:
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"""Auto-close open trades per the active exit policy, from the daily bars.
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Walks the bars after each trade's open. 'time' closes at the initial stop or
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the hold_days-th close; 'trailing' at the trailing/initial stop; 'target' at
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the setup's target or stop (same logic as the outcome evaluator). Trades that
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have hit nothing stay open. Returns the count closed.
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"""
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result = await db.execute(select(PaperTrade).where(PaperTrade.status == "open"))
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open_trades = list(result.scalars().all())
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if not open_trades:
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return 0
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policy = await get_exit_policy(db)
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mode = policy["mode"]
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trail_frac = policy["trailing_pct"] / 100.0
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hold_days = policy["hold_days"]
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closed = 0
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for trade in open_trades:
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bars_result = await db.execute(
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select(
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OHLCVRecord.date, OHLCVRecord.open, OHLCVRecord.high,
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OHLCVRecord.low, OHLCVRecord.close,
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)
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.where(
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OHLCVRecord.ticker_id == trade.ticker_id,
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OHLCVRecord.date > trade.opened_at.date(),
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)
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.order_by(OHLCVRecord.date.asc())
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)
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rows = bars_result.all()
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bars = [Bar(date=d, high=h, low=lo) for d, _, h, lo, _ in rows]
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if not bars:
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continue
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if mode == "time":
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hit = _time_close(trade.direction, trade.stop_loss, hold_days, rows)
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if hit is None:
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continue # neither the stop nor the hold horizon reached yet
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close_price, close_date, reason = hit
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elif mode == "trailing":
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hit = _trailing_close(trade.direction, trade.entry_price, trade.stop_loss, trail_frac, bars)
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if hit is None:
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continue # neither the trailing nor the initial stop reached yet
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close_price, close_date, reason = hit
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else:
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# max_bars beyond the data so a still-open trade returns undecided (not "expired").
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outcome, outcome_date = evaluate_setup_against_bars(
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trade.direction, trade.stop_loss, trade.target, bars, max_bars=len(bars) + 1
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)
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if outcome == OUTCOME_TARGET_HIT:
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close_price, close_date, reason = trade.target, outcome_date, "target"
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elif outcome in (OUTCOME_STOP_HIT, OUTCOME_AMBIGUOUS):
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close_price, close_date, reason = trade.stop_loss, outcome_date, "stop"
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else:
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continue
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trade.status = "closed"
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trade.close_price = float(close_price)
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trade.close_reason = reason
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trade.closed_at = datetime.combine(close_date, datetime.min.time(), tzinfo=timezone.utc)
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closed += 1
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if closed:
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await db.commit()
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return closed
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